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In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that ...

In this tutorial, we discuss formulations and methods such that collaborative fairness, model fairness, and privacy can be fully respected in federated learning ...

Feb 6, 2024 · In this paper, we introduce a novel method called Fair, Robust, and Efficient Client Assessment (FRECA) for quantifying client contributions in FL.

To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statis-.

Federated learning has emerged as an important dis- tributed learning paradigm, where a server aggregates a global model from many client-trained models, ...

Dec 20, 2023 · This paper presents a novel incentive mechanism tailored for fair graph federated learning, integrating incentives derived from both model gradient and payoff.

We propose a novel incentive mechanism tai- lored for fair graph federated learning that provides both model gradients and payoff for agents. Particularly, we.

Customize machine learning algorithms to directly train fair models. Most of these methods assumes a unified available training dataset, which infringes data ...

Aug 14, 2021 · In this tutorial, we discuss formulations and methods such that collaborative fairness, model fairness, and privacy can be fully respected in ...